AI Foundations - Research Co-Op

Introduction

At IBM Research, we are the innovation engine of IBM. Exploring what’s next in computing and shaping the technologies the world will rely on tomorrow. From advancing AI and hybrid cloud to pioneering practical quantum computing, we anticipate challenges and unlock new opportunities for clients, partners, and society. Working in Research means joining a team that accelerates discovery at the intersection of high-performance computing, AI, quantum, and cloud. You’ll collaborate with leading scientists, engineers, and visionaries to push boundaries and turn ideas into reality. With a culture built on curiosity, creativity, and collaboration, IBM Research offers the opportunity to grow your career while contributing to breakthroughs that transform industries and change the world.

Your Role And Responsibilities

• Conduct cutting-edge research and algorithm development to improve the reliability and trustworthiness of large language models (LLMs), including but not limited to:
• Developing methods to detect LLM hallucinations using probabilistic modeling, uncertainty estimation, verbalized uncertainty, retrieval-augmented generation (RAG), and related techniques
• Designing and implementing approaches to reduce LLM hallucinations through fine-tuning, reinforcement learning, novel architectural modifications, or other advanced methods
• Prototype, implement, and evaluate models using modern machine learning frameworks
• Contribute to research publications, technical reports, and project deliverables
  

Preferred Education

Master's Degree

Required Technical And Professional Expertise

• Strong foundational knowledge of large language models, including Transformer architectures and training paradigms
• Proficiency in deep learning frameworks (e.g., PyTorch) and strong programming skills in Python
• Solid understanding of machine learning fundamentals, including optimization, probabilistic modeling, and evaluation methodologies
• Ability to independently design experiments, analyze results, and iterate on research ideas
• Strong problem-solving skills and ability to work in a collaborative research environment
  
  

Preferred Technical And Professional Experience

• Demonstrated research experience in LLMs or closely related areas
• Publications in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP)
• Hands-on experience with training, fine-tuning, or evaluating large-scale language models
• Familiarity with techniques such as reinforcement learning from human feedback (RLHF), retrieval-augmented generation (RAG), or uncertainty quantification
• Experience working on real-world LLM systems or large-scale ML infrastructure